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feat: implement PCA-based normal estimation with parallel processing and comprehensive benchmarks
This commit adds a complete PCA-based normal estimation algorithm for point clouds using KNN and Eigen for robust mathematical computations. ## Core Implementation - **PCA Normal Estimator**: Template-based design supporting different data types and KNN algorithms - **Parallel Processing**: Optional parallel execution using thread pool singleton with 5-11x speedup - **Robust PCA**: Uses Eigen's SelfAdjointEigenSolver for numerical stability - **Multiple KNN Support**: Works with KDTree, Brute Force, and Brute Force Parallel algorithms ## Key Features - **Accuracy**: Maintains <11 degrees error on planar surfaces - **Performance**: KDTree processes 10K points in ~21ms (sequential), ~3ms (parallel) - **Scalability**: Better parallel efficiency with larger point clouds (up to 11x speedup) - **Edge Case Handling**: Graceful fallback for insufficient neighbors or computation failures ## Files Added - `src/include/cpp-toolbox/pcl/norm/base_norm.hpp`: CRTP base class for normal extractors - `src/include/cpp-toolbox/pcl/norm/pca_norm.hpp`: PCA normal extractor interface - `src/include/cpp-toolbox/pcl/norm/impl/pca_norm_impl.hpp`: Complete PCA implementation with Eigen - `test/pcl/norm_test.cpp`: Comprehensive tests (668 assertions covering functionality, accuracy, edge cases) - `benchmark/pcl/norm_benc.cpp`: Detailed parallel vs sequential performance benchmarks ## Build System Updates - Added Eigen dependency to test target for matrix operations - Updated CMakeLists.txt files to include new norm tests and benchmarks - Fixed compilation issues with non-copyable KNN objects ## Code Style Improvements - Unified naming convention: `kParallelThreshold` → `k_parallel_threshold` - Reorganized includes in KNN headers for consistency ## Performance Results - **1K points**: 5.5x speedup (1.32ms → 0.24ms) - **5K points**: 6.6x speedup (9.35ms → 1.42ms) - **10K points**: 7.3x speedup (21.2ms → 2.92ms) - **25K points**: 11.1x speedup (65.9ms → 5.96ms) 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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‎benchmark/CMakeLists.txt‎

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@@ -7,6 +7,7 @@ list(APPEND BENCHMARK_FILES
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file/memory_mapped_file_benc.cpp
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pcl/downsampling_benc.cpp
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pcl/knn_benc.cpp
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pcl/norm_benc.cpp
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${CMAKE_SOURCE_DIR}/test/my_catch2_main.cpp
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)
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‎benchmark/pcl/norm_benc.cpp‎

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#include <catch2/benchmark/catch_benchmark.hpp>
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#include <catch2/catch_test_macros.hpp>
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#include <cpp-toolbox/pcl/norm/pca_norm.hpp>
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#include <cpp-toolbox/pcl/knn/kdtree.hpp>
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#include <cpp-toolbox/pcl/knn/bfknn.hpp>
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#include <cpp-toolbox/pcl/knn/bfknn_parallel.hpp>
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#include <cpp-toolbox/utils/random.hpp>
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#include <cpp-toolbox/utils/timer.hpp>
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#include <iostream>
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#include <memory>
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#include <iomanip>
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#include <cmath>
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using namespace toolbox::pcl;
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using namespace toolbox::types;
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// Helper function to generate random point cloud
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template<typename T>
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point_cloud_t<T> generate_benchmark_cloud(std::size_t num_points, T min_val = -100, T max_val = 100)
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{
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point_cloud_t<T> cloud;
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cloud.points.reserve(num_points);
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toolbox::utils::random_t rng;
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for (std::size_t i = 0; i < num_points; ++i)
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{
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point_t<T> pt;
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pt.x = rng.random<T>(min_val, max_val);
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pt.y = rng.random<T>(min_val, max_val);
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pt.z = rng.random<T>(min_val, max_val);
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cloud.points.push_back(pt);
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}
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return cloud;
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}
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// Helper function to create a planar point cloud for more realistic benchmarking
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template<typename T>
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point_cloud_t<T> generate_planar_benchmark_cloud(std::size_t num_points, T extent = 50.0)
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{
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point_cloud_t<T> cloud;
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cloud.points.reserve(num_points);
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toolbox::utils::random_t rng;
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// Generate points on a plane with some noise
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for (std::size_t i = 0; i < num_points; ++i)
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{
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T x = rng.random<T>(-extent, extent);
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T y = rng.random<T>(-extent, extent);
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T z = rng.random<T>(-2.0, 2.0); // Small noise in z direction
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cloud.points.emplace_back(x, y, z);
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}
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return cloud;
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}
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// Helper function to generate a spherical point cloud
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template<typename T>
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point_cloud_t<T> generate_spherical_benchmark_cloud(std::size_t num_points, T radius = 50.0)
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{
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point_cloud_t<T> cloud;
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cloud.points.reserve(num_points);
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toolbox::utils::random_t rng;
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for (std::size_t i = 0; i < num_points; ++i)
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{
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// Generate random point on sphere surface
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T theta = rng.random<T>(0, 2 * M_PI);
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T phi = rng.random<T>(0, M_PI);
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T x = radius * std::sin(phi) * std::cos(theta);
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T y = radius * std::sin(phi) * std::sin(theta);
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T z = radius * std::cos(phi);
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cloud.points.emplace_back(x, y, z);
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}
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return cloud;
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}
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template<typename DataType, typename KNN>
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void benchmark_norm_computation(const std::string& test_name,
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const point_cloud_t<DataType>& cloud,
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KNN& knn,
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std::size_t num_neighbors,
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bool enable_parallel)
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{
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pca_norm_extractor_t<DataType, KNN> norm_extractor;
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norm_extractor.set_input(cloud);
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norm_extractor.set_knn(knn);
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norm_extractor.set_num_neighbors(num_neighbors);
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norm_extractor.enable_parallel(enable_parallel);
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std::string parallel_suffix = enable_parallel ? " (Parallel)" : " (Sequential)";
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BENCHMARK(test_name + parallel_suffix)
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{
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return norm_extractor.extract();
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};
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}
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TEST_CASE("PCA Normal Estimation Parallel vs Sequential Benchmarks", "[benchmark][pcl][norm]")
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{
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using data_type = float;
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constexpr std::size_t num_neighbors = 15;
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SECTION("Small Point Cloud (1K points)")
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{
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auto cloud = generate_benchmark_cloud<data_type>(1000);
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INFO("Testing with " << cloud.size() << " points, " << num_neighbors << " neighbors");
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auto kdtree1 = kdtree_t<data_type>{};
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auto kdtree2 = kdtree_t<data_type>{};
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benchmark_norm_computation("KDTree Small Cloud", cloud, kdtree1, num_neighbors, false);
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benchmark_norm_computation("KDTree Small Cloud", cloud, kdtree2, num_neighbors, true);
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}
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SECTION("Medium Point Cloud (5K points)")
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{
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auto cloud = generate_benchmark_cloud<data_type>(5000);
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INFO("Testing with " << cloud.size() << " points, " << num_neighbors << " neighbors");
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auto kdtree1 = kdtree_t<data_type>{};
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auto kdtree2 = kdtree_t<data_type>{};
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benchmark_norm_computation("KDTree Medium Cloud", cloud, kdtree1, num_neighbors, false);
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benchmark_norm_computation("KDTree Medium Cloud", cloud, kdtree2, num_neighbors, true);
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}
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SECTION("Large Point Cloud (10K points)")
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{
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auto cloud = generate_benchmark_cloud<data_type>(10000);
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INFO("Testing with " << cloud.size() << " points, " << num_neighbors << " neighbors");
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auto kdtree1 = kdtree_t<data_type>{};
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auto kdtree2 = kdtree_t<data_type>{};
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benchmark_norm_computation("KDTree Large Cloud", cloud, kdtree1, num_neighbors, false);
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benchmark_norm_computation("KDTree Large Cloud", cloud, kdtree2, num_neighbors, true);
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}
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SECTION("Very Large Point Cloud (25K points)")
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{
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auto cloud = generate_benchmark_cloud<data_type>(25000);
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INFO("Testing with " << cloud.size() << " points, " << num_neighbors << " neighbors");
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auto kdtree1 = kdtree_t<data_type>{};
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auto kdtree2 = kdtree_t<data_type>{};
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benchmark_norm_computation("KDTree Very Large Cloud", cloud, kdtree1, num_neighbors, false);
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benchmark_norm_computation("KDTree Very Large Cloud", cloud, kdtree2, num_neighbors, true);
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}
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}
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TEST_CASE("PCA Normal Estimation - Different Point Cloud Types", "[benchmark][pcl][norm]")
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{
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using data_type = float;
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constexpr std::size_t num_neighbors = 12;
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constexpr std::size_t num_points = 8000;
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SECTION("Random Point Cloud")
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{
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auto cloud = generate_benchmark_cloud<data_type>(num_points);
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INFO("Testing random cloud with " << cloud.size() << " points");
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auto kdtree1 = kdtree_t<data_type>{};
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auto kdtree2 = kdtree_t<data_type>{};
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benchmark_norm_computation("Random Cloud", cloud, kdtree1, num_neighbors, false);
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benchmark_norm_computation("Random Cloud", cloud, kdtree2, num_neighbors, true);
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}
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SECTION("Planar Point Cloud")
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{
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auto cloud = generate_planar_benchmark_cloud<data_type>(num_points);
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INFO("Testing planar cloud with " << cloud.size() << " points");
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auto kdtree1 = kdtree_t<data_type>{};
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auto kdtree2 = kdtree_t<data_type>{};
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benchmark_norm_computation("Planar Cloud", cloud, kdtree1, num_neighbors, false);
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benchmark_norm_computation("Planar Cloud", cloud, kdtree2, num_neighbors, true);
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}
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SECTION("Spherical Point Cloud")
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{
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auto cloud = generate_spherical_benchmark_cloud<data_type>(num_points);
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INFO("Testing spherical cloud with " << cloud.size() << " points");
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auto kdtree1 = kdtree_t<data_type>{};
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auto kdtree2 = kdtree_t<data_type>{};
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benchmark_norm_computation("Spherical Cloud", cloud, kdtree1, num_neighbors, false);
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benchmark_norm_computation("Spherical Cloud", cloud, kdtree2, num_neighbors, true);
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}
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}
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TEST_CASE("PCA Normal Estimation - KNN Algorithm Comparison", "[benchmark][pcl][norm]")
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{
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using data_type = float;
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constexpr std::size_t num_neighbors = 10;
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constexpr std::size_t num_points = 5000;
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auto cloud = generate_benchmark_cloud<data_type>(num_points);
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INFO("Comparing KNN algorithms with " << cloud.size() << " points");
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SECTION("KDTree vs Brute Force - Sequential")
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{
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auto kdtree = kdtree_t<data_type>{};
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auto bfknn = bfknn_t<data_type>{};
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benchmark_norm_computation("KDTree", cloud, kdtree, num_neighbors, false);
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benchmark_norm_computation("Brute Force", cloud, bfknn, num_neighbors, false);
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}
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SECTION("KDTree vs Brute Force - Parallel")
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{
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auto kdtree = kdtree_t<data_type>{};
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auto bfknn_parallel = bfknn_parallel_t<data_type>{};
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benchmark_norm_computation("KDTree", cloud, kdtree, num_neighbors, true);
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benchmark_norm_computation("Brute Force Parallel", cloud, bfknn_parallel, num_neighbors, false);
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}
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}
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TEST_CASE("PCA Normal Estimation - Neighbor Count Impact", "[benchmark][pcl][norm]")
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{
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using data_type = float;
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constexpr std::size_t num_points = 6000;
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auto cloud = generate_benchmark_cloud<data_type>(num_points);
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INFO("Testing neighbor count impact with " << cloud.size() << " points");
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SECTION("Sequential - Different Neighbor Counts")
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{
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auto kdtree_5 = kdtree_t<data_type>{};
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auto kdtree_10 = kdtree_t<data_type>{};
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auto kdtree_20 = kdtree_t<data_type>{};
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auto kdtree_30 = kdtree_t<data_type>{};
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benchmark_norm_computation("5 Neighbors", cloud, kdtree_5, 5, false);
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benchmark_norm_computation("10 Neighbors", cloud, kdtree_10, 10, false);
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benchmark_norm_computation("20 Neighbors", cloud, kdtree_20, 20, false);
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benchmark_norm_computation("30 Neighbors", cloud, kdtree_30, 30, false);
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}
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SECTION("Parallel - Different Neighbor Counts")
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{
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auto kdtree_5 = kdtree_t<data_type>{};
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auto kdtree_10 = kdtree_t<data_type>{};
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auto kdtree_20 = kdtree_t<data_type>{};
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auto kdtree_30 = kdtree_t<data_type>{};
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benchmark_norm_computation("5 Neighbors", cloud, kdtree_5, 5, true);
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benchmark_norm_computation("10 Neighbors", cloud, kdtree_10, 10, true);
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benchmark_norm_computation("20 Neighbors", cloud, kdtree_20, 20, true);
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benchmark_norm_computation("30 Neighbors", cloud, kdtree_30, 30, true);
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}
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}
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TEST_CASE("PCA Normal Estimation - Parallel Speedup Analysis", "[benchmark][pcl][norm]")
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{
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using data_type = float;
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constexpr std::size_t num_neighbors = 15;
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// Test with different cloud sizes to analyze scaling
274+
std::vector<std::size_t> cloud_sizes = {2000, 4000, 8000, 15000, 30000};
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for (auto size : cloud_sizes) {
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DYNAMIC_SECTION("Cloud Size: " << size << " points") {
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auto cloud = generate_benchmark_cloud<data_type>(size);
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INFO("Analyzing parallel speedup with " << cloud.size() << " points");
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auto kdtree1 = kdtree_t<data_type>{};
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auto kdtree2 = kdtree_t<data_type>{};
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std::string size_label = "Size " + std::to_string(size);
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benchmark_norm_computation(size_label, cloud, kdtree1, num_neighbors, false);
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benchmark_norm_computation(size_label, cloud, kdtree2, num_neighbors, true);
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}
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}
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}

‎src/include/cpp-toolbox/pcl/filters/impl/voxel_grid_downsampling_impl.hpp‎

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@@ -90,10 +90,10 @@ void voxel_grid_downsampling_t<DataType>::compute_point_cloud_bounds()
9090
}
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9292
// 使用现有的 minmax 工具计算点云边界
93-
constexpr std::size_t kParallelThreshold = 1024;
93+
constexpr std::size_t k_parallel_threshold = 1024;
9494

9595
// 计算点云边界
96-
auto bounds = m_enable_parallel && m_cloud->size() > kParallelThreshold
96+
auto bounds = m_enable_parallel && m_cloud->size() > k_parallel_threshold
9797
? toolbox::types::calculate_minmax_parallel(*m_cloud)
9898
: toolbox::types::calculate_minmax(*m_cloud);
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374374
}
375375

376376
// 定义常量
377-
constexpr std::size_t kParallelThreshold = 1024;
377+
constexpr std::size_t k_parallel_threshold = 1024;
378378
constexpr std::size_t kMaxVoxelsPerThread = 1000;
379379

380380
const std::size_t total_points = m_cloud->size();
@@ -383,7 +383,7 @@ void voxel_grid_downsampling_t<DataType>::filter_impl(point_cloud_ptr output)
383383

384384
// 确定线程数量
385385
const std::size_t num_threads =
386-
m_enable_parallel && total_points > kParallelThreshold
386+
m_enable_parallel && total_points > k_parallel_threshold
387387
? std::thread::hardware_concurrency()
388388
: 1;
389389

@@ -403,7 +403,7 @@ void voxel_grid_downsampling_t<DataType>::filter_impl(point_cloud_ptr output)
403403
}
404404

405405
// 并行或串行处理点云
406-
if (m_enable_parallel && total_points > kParallelThreshold) {
406+
if (m_enable_parallel && total_points > k_parallel_threshold) {
407407
// 计算每个线程处理的点数
408408
const std::size_t points_per_thread =
409409
(total_points + num_threads - 1) / num_threads;
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485485
output->intensity = m_cloud->intensity;
486486

487487
// 并行或串行计算质心
488-
if (m_enable_parallel && num_voxels > kParallelThreshold) {
488+
if (m_enable_parallel && num_voxels > k_parallel_threshold) {
489489
std::vector<std::future<void>> futures;
490490
futures.reserve(num_threads);
491491

‎src/include/cpp-toolbox/pcl/knn/bfknn_parallel.hpp‎

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@@ -1,13 +1,14 @@
11
#pragma once
22

3-
#include <cpp-toolbox/pcl/knn/base_knn.hpp>
43
#include <cpp-toolbox/concurrent/parallel.hpp>
4+
#include <cpp-toolbox/pcl/knn/base_knn.hpp>
55

66
namespace toolbox::pcl
77
{
88

99
template<typename DataType>
10-
class CPP_TOOLBOX_EXPORT bfknn_parallel_t : public base_knn_t<bfknn_parallel_t<DataType>, DataType>
10+
class CPP_TOOLBOX_EXPORT bfknn_parallel_t
11+
: public base_knn_t<bfknn_parallel_t<DataType>, DataType>
1112
{
1213
public:
1314
using data_type = DataType;
@@ -38,7 +39,10 @@ class CPP_TOOLBOX_EXPORT bfknn_parallel_t : public base_knn_t<bfknn_parallel_t<D
3839
std::vector<data_type>& distances);
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4041
void enable_parallel(bool enable) { m_parallel_enabled = enable; }
41-
[[nodiscard]] bool is_parallel_enabled() const noexcept { return m_parallel_enabled; }
42+
[[nodiscard]] bool is_parallel_enabled() const noexcept
43+
{
44+
return m_parallel_enabled;
45+
}
4246

4347
private:
4448
data_type compute_distance(const point_t<data_type>& p1,
@@ -48,7 +52,7 @@ class CPP_TOOLBOX_EXPORT bfknn_parallel_t : public base_knn_t<bfknn_parallel_t<D
4852
point_cloud_ptr m_cloud;
4953
metric_type_t m_metric = metric_type_t::euclidean;
5054
bool m_parallel_enabled = true;
51-
static constexpr std::size_t kParallelThreshold = 1024;
55+
static constexpr std::size_t k_parallel_threshold = 1024;
5256
};
5357

5458
} // namespace toolbox::pcl

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